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Bayesian Blocks: Detecting local variability in time series

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Bayesian Blocks: Detecting and characterizing local variability in time series

Postby owlice » Sun Sep 02, 2012 7:58 pm

Bayesian Blocks: Detecting and characterizing local variability in time series

Abstract: Bayesian Blocks is a time-domain algorithm for detecting localized structures (bursts), revealing pulse shapes within bursts, and generally characterizing intensity variations. The input is raw time series data, in almost any form. Three data modes are elaborated: (1) time-tagged events, (2) binned counts, and (3) measurements at arbitrary times with normal errors. The output is the most probable segmentation of the observation interval into sub-intervals during which the signal is perceptibly constant, i.e. has no statistically significant variations. The idea is not that the source is deemed to actually have this discontinuous, piecewise constant form, rather that such an approximate and generic model is often useful. Treatment of data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multi-variate time series data, analysis of variance, data on the circle, other data modes, and dispersed data are included.

This implementation is exact and replaces the greedy, approximate, and outdated algorithm implemented in BLOCK.

Credit: Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James

Site: https://arxiv.org/src/1207.5578v3/anc
https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S

Bibcode: 2012ascl.soft09001S

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S

ID: ascl:1209.001
Last edited by Ada Coda on Mon Aug 19, 2019 11:24 pm, edited 1 time in total.
Reason: Updated code entry.
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